DARTs：一种用于高维多变量时间序列异常检测的双路径稳健框架
DARTs: A Dual-Path Robust Framework for Anomaly Detection in High-Dimensional Multivariate Time Series
AI总结：
DARTs通过双路径框架和窗口感知机制，提升高维多变量时间序列异常检测的鲁棒性和准确性。
摘要：
多变量时间序列异常检测（MTSAD）旨在准确识别和定位大规模工业控制系统中的复杂异常模式。尽管现有方法在低维场景下能有效识别不同模式，但当从高维噪声时间序列中学习表示时，它们往往无法稳健地捕捉长程时空依赖关系。为解决这些限制，我们提出了DARTs，一种具有窗口感知时空软融合机制的稳健长短期双路径框架，可以主要分解为三个互补组件。具体而言，在短期路径中，我们引入了多视图稀疏图学习器和扩散多关系图单元，它们协作以适应性地捕捉高噪声时间序列中的层次判别短期时空模式。而在长期路径中，我们设计了多尺度时空图构造器，以在高维表示空间中建模显著的长期动态。最后，引入了窗口感知的时空软融合机制，以过滤残余噪声并无缝整合异常模式。在主流数据集上的广泛定性和定量实验结果展示了我们所提出DARTs的优越性和稳健性。还进行了一系列消融研究，以探索我们所提出组件的关键设计因素。我们的代码和模型将很快公开。
英文摘要：
Multivariate time series anomaly detection (MTSAD) aims to accurately identify and localize complex abnormal patterns in the large-scale industrial control systems. While existing approaches excel in recognizing the distinct patterns under the low-dimensional scenarios, they often fail to robustly capture long-range spatiotemporal dependencies when learning representations from the high-dimensional noisy time series. To address these limitations, we propose DARTs, a robust long short-term dual-path framework with window-aware spatiotemporal soft fusion mechanism, which can be primarily decomposed into three complementary components. Specifically, in the short-term path, we introduce a Multi-View Sparse Graph Learner and a Diffusion Multi-Relation Graph Unit that collaborate to adaptively capture hierarchical discriminative short-term spatiotemporal patterns in the high-noise time series. While in the long-term path, we design a Multi-Scale Spatiotemporal Graph Constructor to model salient long-term dynamics within the high-dimensional representation space. Finally, a window-aware spatiotemporal soft-fusion mechanism is introduced to filter the residual noise while seamlessly integrating anomalous patterns. Extensive qualitative and quantitative experimental results across mainstream datasets demonstrate the superiority and robustness of our proposed DARTs. A series of ablation studies are also conducted to explore the crucial design factors of our proposed components. Our code and model will be made publicly open soon.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
